Next-Gen Low-Latency Authentication & Wi-Fi Access Control for Edge Gateways

This project focuses on developing a high-performance authentication and access control system for Wi-Fi networks running on edge gateways. The goal is to replace existing solutions like CoovaChilli and FreeRADIUS, which introduce latency and inefficiencies, with a modern, lightweight system built using Go. Currently, CoovaChilli is responsible for captive portal redirection, DHCP, and firewall control, while FreeRADIUS handles authentication. However, these systems are not optimized for low-latency environments and can slow down user authentication. This project will redesign the authentication flow to improve speed, scalability, and security. Students will develop a Go-based authentication service that integrates with an existing React captive portal and Node.js backend. The new system will use iptables for firewall control, Dnsmasq for DHCP, and Redis for session tracking. Authentication will be managed through a JWT-based system, replacing the traditional RADIUS model. This project provides an opportunity to work with real-world networking challenges, optimize authentication workflows, and build scalable software for embedded edge devices. It combines system architecture, backend development, and networking fundamentals to deliver a production-ready solution that enhances Wi-Fi access control. Students working on this project will gain hands-on experience in networking, security, and backend development while building a real-world authentication system for edge computing. Networking & Wi-Fi Access Control Learn how captive portals work and how to manage Wi-Fi authentication. Work with iptables for traffic control and Dnsmasq for DHCP configuration. Understand how edge devices handle network security and session management. Backend Development & Authentication Systems Build a Go-based authentication service that replaces FreeRADIUS. Implement JWT authentication for a stateless and scalable login system. Use Redis for session tracking to improve performance. Integration with Existing Systems Work with a React-based Captive Portal and integrate it with the new backend. Develop REST APIs and optimize the Node.js backend for handling authentication requests. Ensure seamless interaction between authentication, firewall rules, and session management. Scalability & Performance Optimization Optimize system components to reduce latency and improve response times. Implement efficient request handling using Go’s concurrency model. Design a system that can support multiple concurrent users on edge gateways. Real-World Project Exposure Gain experience working on production-level networking solutions. Learn best practices in security, access control, and embedded systems. Work on a scalable solution that can be deployed in real-world environments. Project Involvement & Meetings Weekly check-ins with the sponsor to review progress and address challenges. Hands-on testing with edge devices to validate system performance. Virtual and in-person meetings for debugging, feedback, and optimization. Final demonstration and presentation of the working system. This project will provide students with a strong foundation in networking, authentication systems, and edge computing, preparing them for careers in cloud infrastructure, cybersecurity, and backend development.

Voice-Activated AI Assistant for Enhanced Visitor Experience

Project Goal The main objective of SmartArena is to enhance the visitor experience at BK Arena by integrating a voice-activated AI assistant into the arena's web application. This AI assistant will provide real-time, personalized support to visitors, helping them navigate the arena, access information about events, amenities, and services, make reservations and orders, and receive personalized recommendations. Problem Statement Visitors to large venues like BK Arena often face challenges such as finding information about events, locating amenities, and making reservations or orders. Traditional methods of accessing this information can be cumbersome and time-consuming, leading to a suboptimal visitor experience. Expected Outcome By the end of this project, learners are expected to achieve the following outcomes: Enhanced Visitor Engagement : Visitors will have an interactive, voice-activated AI assistant that can provide instant responses to their queries, improving their overall experience. Improved Accessibility : The AI assistant will support multiple languages and voice interaction, making it accessible to a diverse audience. Increased Operational Efficiency : The AI assistant will streamline processes such as information dissemination, navigation assistance, and order management, reducing the burden on arena staff. Personalized Visitor Experience : By leveraging user data, the AI assistant will offer personalized recommendations and services, enhancing visitor satisfaction. Seamless Integration : The AI assistant will be seamlessly integrated into the existing web application and edge infrastructure, ensuring reliable and scalable performance.

Modular AI for Real-Time Video Analytics on Edge Devices

The objective is to develop a single, modular AI model for edge devices that can perform multiple real-time video analytics tasks, including customer flow analysis, incident detection, security monitoring, and compliance tracking, while being optimized for edge hardware and ensuring GDPR compliance. Tasks and Activities: Model Development : Build a shared backbone AI model with task-specific outputs for modular functionalities. Optimize the model for edge devices by implementing quantization and pruning techniques. Edge Integration : Develop containerized modules for dynamic task activation on Balena OS. Implement real-time processing for video analytics tasks like object detection, tracking, and incident alerts. GDPR Compliance : Integrate face-blurring and anonymization features into the model for privacy protection. Performance Testing and Optimization : Test and optimize the model across various edge hardware scenarios (e.g., single or multi-camera setups). Ensure the system supports OTA updates for easy deployment and maintenance. Deliverables: A fully integrated, modular AI model capable of performing multiple tasks on edge devices. A containerized system for easy deployment, management, and updates via Balena OS. A GDPR-compliant, real-time video processing system with dynamic task activation and resource allocation.

Edge-Optimized Front-End Development for Distributed File Management and Collaboration

ARED Group Inc. is seeking to develop a responsive front-end interface that seamlessly integrates with their distributed edge infrastructure, utilizing Ceph for storage. The primary goal is to create a user-friendly interface that allows for efficient file management, real-time collaboration, and secure data transfer between edge nodes and end-user devices. This project will enable users to interact with the distributed storage system effortlessly, ensuring that data is accessible and manageable from any device. The interface must be responsive, ensuring optimal performance across various screen sizes and devices. The project will provide learners with an opportunity to apply their knowledge of front-end development, distributed systems, and secure data transfer protocols.